classification.py 文件源码

def knn_classify(self, out_folder_path, training_set, test_set, training_labels, test_labels, k=1, msg=""):
        print("message: " + msg)
        out_file_pre_path = os.path.join(out_folder_path, "knn" + str(k) + msg)  # Any output file should extend this path

        knn_classifier = neighbors.KNeighborsClassifier(k, weights='distance')
        knn_classifier.fit(training_set, training_labels)
        predicted = knn_classifier.predict(test_set)

        success = accuracy_score(test_labels, predicted, normalize=False)
        conf_matrix = self.__retrieve_confusion_matrix(test_labels, predicted, out_file_pre_path)
        return conf_matrix, success